Geospatial Analysis of Elevation in Greece

Introduction

In this analysis, explores spatial and raster data for the Mediterranean region, with a focus on Greece. The objective is to visualize and analyze elevation data within the administrative boundaries of Greece and associate elevation information with key cities.

Load Libraries:

library(sf)          # classes and functions for vector data
library(terra)      # classes and functions for raster data
library(spData)        # load geographic data
library(spDataLarge)   # load larger geographic data
library(dplyr)
library(mapsf)
library(tmap)
library(leaflet) # for interactive maps
library(ggplot2)

Load Raster Data :Loading and plotting a raster file containing elevation data for the Mediterranean region.

raster_file = rast("30n000e_20101117_gmted_mea300.tif")
class(raster_file)

## [1] "SpatRaster"
## attr(,"package")
## [1] "terra"

plot(raster_file, col = terrain.colors(6))

There are some faint distinctions of Greece to the right, Italy in the center, and above it, the Alps visible towards the upper left.

Read Shapefiles : Reading three shapefiles: one for the administrative boundaries of Greece (GRC_adm2.shp), one for their capitals (poleis.shp), and one for general locations (places.shp).

nomoi <- st_read("GRC_ADM2/GRC_adm2.shp")

## Reading layer `GRC_ADM2' from data source 
##   `C:\Users\Stella\OneDrive\Υπολογιστής\[02] Διερευνητική Ανάλυση και Οπτικοποίηση Δεδομένων\hw4\data μέρος β\GRC_ADM2\GRC_ADM2.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 77 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 19.3726 ymin: 34.8009 xmax: 29.60684 ymax: 41.74889
## Geodetic CRS:  WGS 84

poleis <- st_read("poleis/poleis.shp")

## Reading layer `poleis' from data source 
##   `C:\Users\Stella\OneDrive\Υπολογιστής\[02] Διερευνητική Ανάλυση και Οπτικοποίηση Δεδομένων\hw4\data μέρος β\poleis\poleis.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 51 features and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 149581.4 ymin: 3895170 xmax: 878802.9 ymax: 4555185
## Projected CRS: GGRS87 / Greek Grid

places <- st_read("places/places.shp")

## Reading layer `places' from data source 
##   `C:\Users\Stella\OneDrive\Υπολογιστής\[02] Διερευνητική Ανάλυση και Οπτικοποίηση Δεδομένων\hw4\data μέρος β\places\places.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 4326 features and 4 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 19.39397 ymin: 34.84761 xmax: 27.2865 ymax: 41.72253
## Geodetic CRS:  WGS 84

An important observation is that the nomoi file comprises 77 administrative regions, whereas the capitals consist of 51. This disparity will be revisited later. Furthermore, it’s noteworthy that the geometry of nomoi is multipolygon using longitude and latitude, while the capitals and places have point geometries and are projections in Greek Grid. This aspect is addressed further down in the analysis to ensure a cohesive representation when plotting them on the same map.

Raster of Greece

Crop Raster to Greece’s Bounding Box:

Extracting the bounding box (the rectangular area that contains all the points) of the administrative boundaries of Greece.

# Get the bounding box of Greece
bbox_greece <- st_bbox(nomoi)

Using this bounding box to crop the elevation data raster to include only the area of Greece.

# Crop raster to the bounding box of Greece
raster_greece <- crop(raster_file, bbox_greece)

Mask Raster with Greece’s Geometry:

Using the actual geographical shape of Greece to mask the elevation data raster. This means keeping only the elevation values that fall within the borders of Greece.

# Mask raster using the geometry of Greece
raster_greece <- mask(raster_greece, nomoi)

Plot the resulting raster: Finally, plotting the newly created raster file, which now contains elevation data only for Greece.

plot(raster_greece, col = terrain.colors(6))

The towering peak of Mount Olympus, the highest point in Greece, prominently marks the map with its distinctive white hue. Other smaller white points, though present, are less conspicuous due to their limited area size.

Greek Regional Capitals as proportional points

City Elevation Analysis: To accurately depict city locations on the regional map, it is imperative to transform the city coordinate reference system to match the longitude-latitude format used for the regions:

poleis_latlon <- st_transform(poleis, crs(raster_greece))

Then calculating elevation values for each city(regional capitals) within Greece:

# Extract elevation values from the raster map at the locations of cities
elev_values <- extract(raster_greece, poleis_latlon)


# Add elevation values in poleis_latlon
poleis_latlon$elevation <- elev_values

Visualization of Administrative Boundaries and Cities with Elevation Information: Finally, plotting the administrative boundaries of Greece and overlay capitals with elevation information, represented by red points whose size is proportional to the elevation:

# Extract the elevation and normalize it
cex <-poleis_latlon$elevation[2] / (max(poleis_latlon$elevation)/2)

# Plot the raster map of Greece
plot(raster_greece, col = terrain.colors(6), reset = FALSE)

# Plot the boundaries of the regions ("nomoi") on top of the raster map
plot(nomoi["geometry"],add = TRUE,axes = TRUE)

# Plot the red points representing the regional capitals on top of the map
# Use the normalized elevation value to adjust the size of the points
plot(poleis_latlon[0], add = TRUE, col = "red", pch = 16, cex = cex[,1])

(Some points may be challenging to observe due to their smaller size in comparison to the larger ones)

The majority of higher-elevated cities are concentrated in the northern regions of the country. Among the capitals, Karpenisi boasts the highest elevation. This finding aligns with the known geography of central Greece. Nestled amidst mountainous landscapes, as illustrated in the elevation map of Greece, Karpenisi is situated in proximity to the Pindos mountain range. Notably, the capitals situated near the Pindos mountain range exhibit higher elevations, whereas those situated near the sea generally attain lower elevations. Reinforcing this, the capital with the lowest elevation is Leykada.

Moreover, it is probable that regions situated near the sea would instinctively position their capitals near ports, whereas inland areas, lacking direct access to the sea, may have historically chosen elevated locations for enhanced protection. This strategic decision-making traces back to ancient times, contributing to the development of these areas that were eventually established as capitals in the modern era.

Regional Mean Elevation and Standard Deviation

Data Preparation:

# Extract elevation values for each region
elev_values <- extract(raster_greece, nomoi)

# Factorize the ID column to perform a group by on it
elev_values$ID <- factor(elev_values$ID)


# Rename the elevation column
elev_values <- elev_values %>%
  rename_at(vars(2), ~ "elevation")

# Group by the "ID" column and Summarize the grouped data (calculate mean and sd)
elev_values <- elev_values %>% group_by(ID) %>% summarize(mean_value = mean(elevation),sd_value = sd(elevation))


# Add mean and sd to the original nomoi dataset
nomoi$mean_elevation <- elev_values[[2]]
nomoi$sd_elev <- elev_values[[3]]

The dataset nomoi now includes two new columns, mean_elevation and sd_elev representing the mean and standard deviation of elevation for each region.

Visualizing Mean Elevation
Visualizing the geographic distribution of mean elevation across administrative regions in Greece:

mf_theme("default")

mf_map(
  x = nomoi,
  var = "mean_elevation",
  type = "choro",
  breaks = "geom",
  nbreaks = 5,
  pal = terrain.colors(8, alpha=1, rev = FALSE),
  border = "white",
  lwd = 0.5,
  leg_pos = "topright",
  leg_title = "Mean Elevation",
)

The choropleth map provides a visual representation of the mean elevation across different administrative regions in Greece. Regions with higher mean elevations are depicted with distinct colors, allowing for a quick and intuitive understanding of the geographic patterns.

Comments:
As anticipated, the highest mean elevation is notably found in the areas surrounding the Pindos mountain range. As one moves away from the central mainland towards the coast, there is a discernible decrease in the mean elevation.

Two intriguing exceptions to this trend are the islands of Crete and Ikaria, that stand out for their remarkable mountainous terrain, defying the typical expectations for an island.

Visualizing Standard Deviation of Elevation
Similarly, a choropleth map to visualize the standard deviation of elevation across administrative regions:

mf_theme("default")

mf_map(
  x = nomoi,
  var = "sd_elev",
  type = "choro",
  breaks = "geom",
  nbreaks = 5,
  pal = terrain.colors(8, alpha=1, rev = FALSE),
  border = "white",
  lwd = 0.5,
  leg_pos = "topright",
  leg_title = "Standar Deviation of Elevation"
)

Comments:
Once again, the Pindos mountain range stands out as the focal point where elevated standard deviation values are consistently noted. This observation implies a notably diverse topography within these regions.

In fact, the majority of Greece showcases a varied landscape, featuring a mix of elevated and lower terrains across its expanse. This characteristic is emblematic of a mountainous terrain enveloped by the sea, with coastlines in close proximity, contributing to the rich topographical tapestry of the country.

Absolute Difference

Spatially associating each region with its corresponding capital, determining the region to which each capital belongs. Then, computing the absolute difference between the mean elevation of the region and the elevation of its capital.

# Ensure the validity of administrative region geometries
nomoi <- st_make_valid(nomoi)

# Spatially join cities with administrative regions based on the "within" relationship
nomoi_with_capitals <- st_join(poleis_latlon,nomoi, join = st_within)

# Calculate absolute difference in elevation between cities and their associated administrative regions
nomoi_with_capitals$abs_diff_elevation <- unlist(abs(nomoi_with_capitals$elevation[2] - nomoi_with_capitals$mean_elevation))

Data preprocessing: As discovered previously the regions in the data are more than the capitals. Thus, filtering the dataframe nomoi to keep only the regions with capitals:

# Find the intersection of feature_id between nomoi and nomoi_with_capitals
inter <- intersect(nomoi$feature_id,nomoi_with_capitals$feature_id)

# Filter nomoi based on the common feature_id values obtained from the intersection
filtered_nomoi <- nomoi %>%
  filter(feature_id %in% inter)

# Add a new column 'abs_diff_elevation' to filtered_nomoi and populate it with corresponding values from nomoi_with_capitals
filtered_nomoi$abs_diff_elevation <- nomoi_with_capitals$abs_diff_elevation[match(filtered_nomoi$feature_id, nomoi_with_capitals$feature_id)]

The extra regions are just further sub divisions of larger regions (e.g. Attica -> West,North,South,East Attica) or errors:

diff <- nomoi %>%
  filter(feature_id %in% setdiff(nomoi$feature_id,nomoi_with_capitals$feature_id))

diff$NAME

##  [1] "Regional Unit North Athens"         "Regional Unit of West Attica"      
##  [3] "Mykonos Regional Unit"              "Karpathos Regional Unit"           
##  [5] "???????????? ??????? ???? - ??????" "???????????? ??????? ????????"     
##  [7] "Regional Unit of Lemnos"            "Regional Unit of Ikaria"           
##  [9] "Regional Unit of Piraeus"           "Regional Unit of West Athens"      
## [11] "???????????? ??????? ??????"        "???????????? ??????? ??"           
## [13] "???????????? ??????? ???????"       "Thira Regional Unit"               
## [15] "Regional Unit of Sporades"          "Paros Regional Unit"               
## [17] "Milos Regional Unit"                "Milos Regional Unit"               
## [19] "Regional Unit of East Attica"       "Naxos Regional Unit"               
## [21] "Thasos Regional Unit"               "Ithaca Regional Unit"              
## [23] "???????????? ??????? ?????"         "Regional Unit of South Athens"     
## [25] "???????????? ??????? ?????"         "Regional Unit of Islands"

Visualizing Absolute Difference of Elevation: A choropleth map to visualize the absolute difference of elevation between the regions and their capitals:

mf_theme("default")
# plot population density
mf_map(
  x = filtered_nomoi,
  var = "abs_diff_elevation",
  type = "choro",
  breaks = "geom",
  nbreaks = 5,
  pal = terrain.colors(8, alpha=1, rev = FALSE),
  border = "white",
  lwd = 0.5,
  leg_pos = "topright",
  leg_title = "Absolute Difference"
)

The substantial variance between the mean elevation of regions and the elevations of their respective capitals is prevalent across many areas. While most capitals are situated near sea level and boast relatively low altitudes, the presence of towering mountains in these regions significantly raises the overall mean elevation. This phenomenon results in a pronounced disparity. Even capitals positioned away from coastal areas exhibit a substantial deviation from the highest points within the region. With the exception only of a handful of capitals which align with the general altitudes prevalent in their respective regions.

Top 10 regions

The top 10 regions based on the mean elevation:

nomoi[order(nomoi$mean_elevation, decreasing = TRUE), "NAME"][1:10,]$NAME

##  [1] "Kastoria Regional Unit"     "Regional Unit of Evrytania"
##  [3] "Florina Regional Unit"      "Ioannina Regional Unit"    
##  [5] "Grevena Regional Unit"      "Regional Unit of Phocis"   
##  [7] "Regional Unit of Kozani"    "Arcadia Regional Unit"     
##  [9] "Regional Unit of Trikala"   "Drama Regional Unit"

The top 10 regions based on the standard deviation of elevation:

nomoi[order(nomoi$sd_elev, decreasing = TRUE), "NAME"][1:10,]$NAME

##  [1] "Imathia Regional Unit"    "Pieria Regional Unit"    
##  [3] "Regional Unit of Trikala" "Pella Regional Unit"     
##  [5] "Regional Unit of Phocis"  "Chania Regional Unit"    
##  [7] "Arta Regional Unit"       "Achaea Regional Unit"    
##  [9] "Karditsa Regional Unit"   "Ioannina Regional Unit"

Visualizing Altitudes Above 1500 Meters: Inhabited and Uninhabited Locations in Greece

Preprocessing:
Converting the reference system of places to longitude-latitude:

places_latlon <- st_transform(places, crs(raster_greece))

Calculating elevation values for each place within Greece:

elev_values <- extract(raster_greece, places_latlon)
colnames(elev_values) <- c("ID","value")

# Add elevation values in places_latlon
places_latlon$elevation <- elev_values

Upon examination, it becomes evident that certain data points exhibit discrepancies; they either represent errors, given that they do not correspond to locations within Greece, or they are absent from the regional data used to derive the elevation raster for Greece. Consequently, the elevation values for these data points are recorded as NA:

places_latlon[is.na((places_latlon[["elevation"]][,2])),]

## Simple feature collection with 100 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 19.49628 ymin: 35.61053 xmax: 27.04166 ymax: 41.72253
## Geodetic CRS:  WGS 84
## First 10 features:
##        osm_id                          name    type population
## 89   61886594                      Astakida  island          0
## 107  65035962         Ψυττάλεια (Psyttalia)  island          0
## 119  83942427                       Pelouzo  island          0
## 120  83942679                  Pontikonisia  island          0
## 124  83947121 Άγιος Ιωάννης (Agios Ioannis)  island          0
## 478 258063721              Gramvousa Island  island          0
## 669 273814587                    Генералово village          0
## 675 273874873                    Горна Арда village          0
## 676 273877347              Капитан Андреево village          0
## 785 282497732                   Marathonisi  island          0
##                      geometry elevation.ID elevation.value
## 89  POINT (26.82026 35.88547)           89              NA
## 107 POINT (23.58804 37.94123)          107              NA
## 119 POINT (20.94805 37.70375)          119              NA
## 120  POINT (20.8625 37.68604)          120              NA
## 124  POINT (20.62135 37.8204)          124              NA
## 478 POINT (23.57941 35.61053)          478              NA
## 669 POINT (26.27687 41.72253)          669              NA
## 675 POINT (24.60772 41.44903)          675              NA
## 676 POINT (26.32017 41.72034)          676              NA
## 785 POINT (20.86908 37.68582)          785              NA

Among these locations, the ones which are located within Greece exclusively comprise small islands, as discerned from their zero populations, unlikely to surpass 1500 meters. Consequently, these islands can be confidently omitted from consideration.

# Filter places_latlon to include only locations with elevation higher than 1500 meters
places_higher_than_1500 <- na.omit(places_latlon[places_latlon[["elevation"]][,2] > 1500,])

# Keep only inhabited places (population > 0) among those with elevation higher than 1500
inhabited <- subset(places_higher_than_1500, population > 0)

# Keep only uninhabited places (population == 0) among those with elevation higher than 1500
uninhabited <- subset(places_higher_than_1500, population == 0)

inhabited

## Simple feature collection with 1 feature and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 21.01633 ymin: 40.10259 xmax: 21.01633 ymax: 40.10259
## Geodetic CRS:  WGS 84
##         osm_id     name   type population                  geometry
## 1489 296596658 Σαμαρίνα hamlet        701 POINT (21.01633 40.10259)
##      elevation.ID elevation.value
## 1489         1489            1533

uninhabited

## Simple feature collection with 6 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 21.10604 ymin: 39.576 xmax: 21.31684 ymax: 40.84674
## Geodetic CRS:  WGS 84
##         osm_id               name   type population                  geometry
## 1174 286918633           Αθαμανία region          0   POINT (21.18557 39.576)
## 1177 287220488     Oros Triklario region          0 POINT (21.10604 40.67903)
## 1190 287224403    Oros Varnoundas region          0 POINT (21.23711 40.84674)
## 1191 287224488              Vitsi   peak          0 POINT (21.31684 40.72146)
## 1192 287224694      Verno (Vitsi) region          0 POINT (21.31392 40.75021)
## 2998 344746654 Λάκμος (Περιστέρι) region          0   POINT (21.114 39.67118)
##      elevation.ID elevation.value
## 1174         1174            1759
## 1177         1177            1606
## 1190         1190            1969
## 1191         1191            1542
## 1192         1192            1581
## 2998         2998            1964

tmap_mode("view")

names(raster_greece) <- "elevation"

tm_shape(raster_greece) + tm_raster("elevation", palette = terrain.colors(6)) + tm_shape(nomoi) + tm_borders() + tm_shape(uninhabited) + tm_dots(col = "red", size = 0.1) +tm_text("name", just = "top") + tm_shape(inhabited) + tm_dots(col = "green", size = 0.1) +   tm_text("name", just = "top")

Comments:

There are a total of 7 locations situated at elevations exceeding 1500 meters, and intriguingly, Samarina stands as the lowest among them, being the sole inhabited one.

Settling in areas above 1500 meters is unusual due to harsh environmental conditions, including extreme cold, limited resources, and lower oxygen levels, making sustained habitation challenging. However, factors such as historical significance or unique cultural and economic circumstances may contribute to rare instances of population concentration in such elevated areas, as exemplified by Samarina.

This particular location likely developed over the years as its challenges provided safety to its inhabitants, enabling them to survive, unlike the other six locations, which are even higher and presumably have harsher conditions. This trend is reminiscent of the Turkish occupation in Greece, where Greeks often isolated themselves in small remote communities, especially in mountains, to escape their oppressors.

Despite signs of human life dating back to primitive eras, the more recent wars have brought fame to the location for the contribution of its inhabitants to the revolution, as immortalized in the Greek folklore song “Children of Samarina”. The area’s historical resilience and cultural significance underscore its enduring importance in the region, making it a compelling tourist attraction as well.

Raster Reclassification

# Class breaks
breaks <- c(0, 500, 1000, 1500, 2000, 2500, 3000)
m <- c(0, 500,
       500, 1000,
       1000, 1500,
       1500, 2000,
       2000, 2500,
       2500, 3000)

# Create Matrix
rcl <- cbind(breaks[-length(breaks)], breaks[-1])
rclmat <- matrix(m, ncol=2, byrow=TRUE)

# Classify raster
raster_reclassified <- classify(raster_greece, rclmat, include.lowest=TRUE)


plot(raster_reclassified, reset = FALSE, col = terrain.colors(6), main = "Reclassified Elevation")

Grevena - Ioannina Line

Coordinates of Grevena and Ioannina:

# Get coordinates
grevena_coord <- st_coordinates(subset(poleis_latlon, NAME == "Grebena"))
ioannina_coord <- st_coordinates(subset(poleis_latlon, NAME == "Ioannina"))

# Combine the coordinates into a single data frame 'x'
x <-data.frame(rbind(grevena_coord,ioannina_coord))
colnames(x) <- c("lon", "lat")

points <- st_as_sf(x, coords = c("lon", "lat"), crs = 4326)

Create line and plot it on the map:

# Combine Grebena and Ioannina coordinates into a linestring object, assign CRS, and create an sf object
grevion_transect = rbind(grevena_coord,ioannina_coord) |>
  st_linestring() |> 
  st_sfc(crs = crs(raster_greece)) |>
  st_sf(geometry = _)

# Add an 'id' column to grevion_transect
grevion_transect$id = 1:nrow(grevion_transect)

# Segmentize the linestring into smaller segments with a maximum length of 250 units
grevion_transect = st_segmentize(grevion_transect, dfMaxLength = 250)

# Convert the linestring to points
grevion_transect = st_cast(grevion_transect, "POINT")

# Calculate distances along the transect and add as a 'dist' column
grevion_transect = grevion_transect |> 
  group_by(id) |> 
  mutate(dist = st_distance(geometry)[, 1]) 

# Extract elevation values from the raster data for the transect points
grevion_elev = terra::extract(raster_greece, grevion_transect)

# Combine the transect points with elevation values
grevion_transect = cbind(grevion_transect, grevion_elev)

plot(raster_reclassified, reset = FALSE, col = terrain.colors(6))
plot(grevion_transect,add = TRUE, col = "red",cex = 0.2)
plot(points, add = TRUE, col = "blue", pch = 19)
text(x = x$lon, y = x$lat, labels = c("Grebena", "Ioannina"), pos = 3, offset = 0.5, col = "black")

plot(grevion_transect$dist,grevion_transect[[4]],xlab = "Distance",ylab = "Elevation", type = "l")

# Calculate midpoint and add a vertical line at the midpoint
midpoint <- (max(grevion_transect$dist) + min(grevion_transect$dist)) / 2
abline(v = midpoint, col = "red", lty = 2)

The straight line linking ‘Grebena’ and ‘Ioannina’ traverses a mountain range, with altitudes steadily rising until approximately the midpoint, after which they begin to decline. In proximity to ‘Ioannina,’ there is a renewed ascent in altitude, followed by a subsequent descent. This indicates the presence of a valley nestled between an extensive mountain range and a solitary high peak just before the city, namely ‘Mitsikeli.’ The abrupt decline is likely influenced by the presence of a lake situated at the foothills of the mountain, right in front of the city. Additionally, the trajectory of the line creates numerous small plateaus along its course indicating a diverse and dynamic landscape.

Thiva - Athens Line

Coordinates of Thiva and Athens:

# Get coordinates
thiva_coord <- st_coordinates(subset(places_latlon, name == "Θήβα (Thiva)"))
athens_coord <- st_coordinates(subset(poleis_latlon, NAME == "Athens"))

# Combine the coordinates into a single data frame 'x'
x <-data.frame(rbind(thiva_coord,athens_coord))
colnames(x) <- c("lon", "lat")

points <- st_as_sf(x, coords = c("lon", "lat"), crs = 4326)

Create line and plot it on the map:

# Combine Thiva and Athens coordinates into a linestring object, assign CRS, and create an sf object
thivath_transect = rbind(thiva_coord,athens_coord) |>
  st_linestring() |> 
  st_sfc(crs = crs(raster_greece)) |>
  st_sf(geometry = _)

# Add an 'id' column to grevion_transect
thivath_transect$id = 1:nrow(thivath_transect)

# Segmentize the linestring into smaller segments with a maximum length of 250 units
thivath_transect = st_segmentize(thivath_transect, dfMaxLength = 250)

# Convert the linestring to points
thivath_transect = st_cast(thivath_transect, "POINT")

# Calculate distances along the transect and add as a 'dist' column
thivath_transect = thivath_transect |> 
  group_by(id) |> 
  mutate(dist = st_distance(geometry)[, 1]) 

# Extract elevation values from the raster data for the transect points
thivath_elev = terra::extract(raster_greece, thivath_transect)

# Combine the transect points with elevation values
thivath_transect = cbind(thivath_transect, thivath_elev)

plot(raster_reclassified, reset = FALSE, col = terrain.colors(6))
plot(thivath_transect,add = TRUE, col = "red",cex = 0.2)
plot(points, add = TRUE, col = "blue", pch = 19)
text(x = x$lon, y = x$lat, labels = c("Thiva", "Athens"), pos = 3, offset = 0.1, col = "black")

plot(thivath_transect$dist,thivath_transect[[4]],xlab = "Distance",ylab = "Elevation", type = "l")


# Calculate midpoint and add a vertical line at the midpoint
midpoint <- (max(thivath_transect$dist) + min(thivath_transect$dist)) / 2
abline(v = midpoint, col = "red", lty = 2)

The direct line connecting ‘Thiva’ and ‘Athens’ demonstrates a gradual ascent in altitude that begins to decline well before reaching the midpoint. There is only a minor elevation just before approaching ‘Athens’. Again, rather than a rugged, sharply defined landscape, these small plateaus suggest a more subdued and gently undulating topography

Extra

1: Follows a brief exploration of a shapefile containing the locations of ancient Greek theaters, with an examination of its potential correlation with the topographical elevation of the land.

Basemap Link: https://www.ascsa.edu.gr/pdf/uploads/CorinthMaps/Basemap.zip

theaters <- st_read("AncientTheaters/AncientTheaters.shp",options = "ENCODING=WINDOWS-1252")

## options:        ENCODING=WINDOWS-1252 
## Reading layer `AncientTheaters' from data source 
##   `C:\Users\Stella\OneDrive\Υπολογιστής\[02] Διερευνητική Ανάλυση και Οπτικοποίηση Δεδομένων\hw4\data μέρος β\AncientTheaters\AncientTheaters.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 43 features and 3 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 181924.4 ymin: 3878798 xmax: 792793.8 ymax: 4539998
## Projected CRS: GGRS87 / Greek Grid

theaters <- st_transform(theaters, crs(raster_greece))

plot(raster_greece, col = terrain.colors(6), reset = FALSE)
plot(st_geometry(theaters), add = TRUE, col = "magenta", pch = 16)

basemap = rast("Basemap.tif")
theaters <- st_transform(theaters, crs(basemap))
plot(basemap, reset = FALSE)
plot(st_geometry(theaters), add = TRUE, col = "magenta", pch = 16)

Ancient Greek theaters were typically built in locations with specific considerations for acoustics, aesthetics, and practicality.

The primary observation that stands out is the evident proximity of these theaters to the sea. It appears that theaters were strategically positioned to capitalize on scenic views, whether it be overlooking the sea or a picturesque landscape. This deliberate placement not only enhanced the overall experience for the audience but also significantly contributed to the aesthetic allure of the theaters.

The second noteworthy observation is the proximity of these theaters to, though not directly atop, mountainous areas. This aligns with the expectation, as Greek theaters were commonly built on natural slopes or hillsides. The inherent incline served as an ideal foundation for seating arrangements, guaranteeing optimal visibility for the audience. Additionally, the slope played a crucial role in enhancing acoustics, facilitating effective sound propagation throughout the theater.

2: Follows a brief exploration of a shapefile containing the railway lines in Greece, with an examination of their potential correlation with the topographical elevation of the land.

railways <- st_read("railways/railways.shp")

## Reading layer `railways' from data source 
##   `C:\Users\Stella\OneDrive\Υπολογιστής\[02] Διερευνητική Ανάλυση και Οπτικοποίηση Δεδομένων\hw4\data μέρος β\railways\railways.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 4624 features and 3 fields
## Geometry type: LINESTRING
## Dimension:     XY
## Bounding box:  xmin: 21.14558 ymin: 35.14534 xmax: 28.18489 ymax: 41.765
## Geodetic CRS:  WGS 84

railways <- st_transform(railways, crs(raster_greece))

plot(raster_greece, col = terrain.colors(6), reset = FALSE)
plot(st_geometry(railways), add = TRUE, col = "red", pch = 16)

It is evident that the railway lines strategically avoid the denser and higher elevated mountainous regions, primarily traversing low-altitude flatlands or coastlines. While in the Peloponnese, it may seem to traverse the mountain range, upon closer inspection, it becomes apparent that the route strategically follows lower altitude valleys.